CN110869867A - Method for verifying a digital map of a vehicle with a high degree of automation, corresponding device and computer program - Google Patents
Method for verifying a digital map of a vehicle with a high degree of automation, corresponding device and computer program Download PDFInfo
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- CN110869867A CN110869867A CN201880045461.7A CN201880045461A CN110869867A CN 110869867 A CN110869867 A CN 110869867A CN 201880045461 A CN201880045461 A CN 201880045461A CN 110869867 A CN110869867 A CN 110869867A
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- 238000000034 method Methods 0.000 title claims abstract description 34
- 238000004590 computer program Methods 0.000 title claims abstract description 7
- 238000013507 mapping Methods 0.000 claims description 10
- 238000001514 detection method Methods 0.000 claims description 7
- 230000003068 static effect Effects 0.000 claims description 6
- 238000004891 communication Methods 0.000 claims description 3
- 239000000463 material Substances 0.000 claims description 3
- 230000000694 effects Effects 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 239000000523 sample Substances 0.000 description 2
- 230000011664 signaling Effects 0.000 description 2
- 238000012795 verification Methods 0.000 description 2
- 230000002747 voluntary effect Effects 0.000 description 2
- 238000010276 construction Methods 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/38—Electronic maps specially adapted for navigation; Updating thereof
- G01C21/3804—Creation or updating of map data
- G01C21/3833—Creation or updating of map data characterised by the source of data
- G01C21/3841—Data obtained from two or more sources, e.g. probe vehicles
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0108—Measuring and analyzing of parameters relative to traffic conditions based on the source of data
- G08G1/0112—Measuring and analyzing of parameters relative to traffic conditions based on the source of data from the vehicle, e.g. floating car data [FCD]
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0125—Traffic data processing
- G08G1/0129—Traffic data processing for creating historical data or processing based on historical data
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
- G08G1/0145—Measuring and analyzing of parameters relative to traffic conditions for specific applications for active traffic flow control
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/30—Services specially adapted for particular environments, situations or purposes
- H04W4/40—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P]
- H04W4/46—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P] for vehicle-to-vehicle communication [V2V]
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
- G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/42—Determining position
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/02—Services making use of location information
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- General Physics & Mathematics (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Automation & Control Theory (AREA)
- Traffic Control Systems (AREA)
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Abstract
The invention relates to a method for verifying a digital map of a highly automated vehicle (HAF), in particular a highly automated vehicle, comprising the following steps: s1 providing a digital map, preferably a high accuracy digital map; s2 determining a current reference position and locating the reference position in the digital map; s3 determining at least one actual feature characteristic of a feature in the surroundings of the reference location, wherein the determination is performed by means of at least one information source; s4 compares the actual characteristic feature of the feature with the expected characteristic feature and finds at least one difference as a result of the comparison. The invention also relates to a corresponding device and to a computer program.
Description
Technical Field
The invention relates to a method for verifying a digital map of a highly automated vehicle (HAF), in particular a highly automated vehicle, and to a device for this purpose.
Background
In view of the increasing degree of automation of vehicles, increasingly complex driver assistance systems are used. For such driver assistance systems and functions, for example in highly automated or fully automated driving, a large number of sensors are required in the vehicle, which sensors enable an accurate detection of the vehicle environment.
In the following, "highly automated" is understood to mean all of the following degrees of automation: the degree of automation corresponds to automated longitudinal and transverse guidance with greater system responsibility, for example highly automated and fully automated driving, in the sense of the german federal highway institute (BASt).
In the prior art, various possibilities for carrying out methods for operating highly automated vehicles (HAF) are known. In order to increase the positioning of highly automated vehicles (HAF) in digital maps, it is necessary to be able to ensure the accuracy of the digital maps, wherein the following problems arise here: short-term road segment changes, for example due to construction sites, accidents or other types of situations, cannot be or can only be incompletely taken into account in digital maps, or occur so short that highly automated vehicles (HAF), in particular highly automated vehicles, cannot handle these short-term changes quickly enough and must return the vehicle control to the driver. This may be undesirable in terms of traffic safety and may also be critical if necessary.
In order to control the vehicle to a high degree of automation in as many cases as possible, it is necessary to provide a digital map which is error-free to the greatest extent and corresponds to the actual situation.
Another related aspect results from the following: conventional mapping methods, for example by means of motor vehicles, aircraft or satellites, are very costly. It is therefore desirable to be able to estimate as accurately as possible: whether a map section of the digital map must be redrawn. The basis for this estimation is always a description of the accuracy of the digital map.
On the basis of sensor data on the current of a digital map
The persuasive aspect of the estimation of (c) should also be noted: each sensor type used to verify the map is subject to certain specific limitations. For example, the camera is limited as follows: the camera can only photograph an object that is not blocked by other objects at the time of photographing or cannot be recognized due to the influence of light. This effect can be generalized, for example, under the concept of robustness of detection by the sensor.
It is therefore the object of the present invention to provide an improved method for validating a digital map of a vehicle (HAF) with a high degree of automation, in particular a highly automated vehicle, and an improved device for this purpose, by means of which the currency of the digital map can be reliably determined and which allows an accurate estimation of: whether a redrawing of the map section is necessary, wherein influences caused by sensor-specific limitations should be avoided to the greatest possible extent.
Disclosure of Invention
This object is achieved by the corresponding subject matter of the independent claims. Advantageous embodiments of the invention are the subject matter of the dependent claims.
According to an aspect of the invention, a method for validating a digital map of a highly automated vehicle (HAF), in particular of a highly automated vehicle, is provided, comprising the steps of:
s1 providing a digital map, preferably a high accuracy digital map;
s2 determining a current (aktuelle) reference location and locating the reference location in a digital map;
s3 determining at least one actual feature characteristic of a feature in the surroundings of the reference location, wherein the determination is performed by means of at least one information source;
s4 compares the actual characteristic features of the feature with the expected characteristic features and takes at least one difference as a result of the comparison.
Preferably, the method according to the invention comprises in a further step S5 verifying the digital map at least partly on the basis of the difference, wherein the digital map is ranked as not current if the difference reaches or exceeds a prescribed threshold of deviation and as current if the difference remains below the prescribed threshold of deviation.
Step S6 includes using a plurality of information sources, wherein the information sources include at least one or more from the following group of information sources:
a vehicle-to-infrastructure system (C2I) that sends data to or collects data from the vehicle (e.g., over WLAN, LTE);
a vehicle-to-vehicle system (C2C) that transmits data to other vehicles, preferably over a wireless communication network (WLAN) or LTE;
a navigation system that stores road strike, grade, lane and infrastructure information in map material;
the system comprises a database in the Internet, wherein road data are preferably stored in the database;
a database in a vehicle's own system in which data can be stored for a long period of time;
a vehicle system, preferably a Head Unit, having access to the internet and having access to current data from a database.
A high-accuracy map for highly automated or fully automated driving, in which data for a positioning task, preferably an object having a position and a size, are stored;
a driver assistance system comprising one or more systems from the following group:
Lane-Keeping-Support (LKS), which seeks a Lane, preferably by means of a camera-based system, and redirects the vehicle back into the Lane without voluntary departure from the Lane;
a traffic sign system that finds a prescribed speed based on a vision system (preferably a video camera);
an object detection system, preferably using a vision sensor (particularly preferably a video camera); a smart phone (in particular a camera of a smart phone).
Such sources of information are almost ubiquitous in modern society. This may be, for example, a number of highly automated or highly automated vehicles traveling daily, cameras installed at all critical infrastructure nodes, or shots taken by users with smart phones worldwide that are uploaded to a central site of the network (e.g., in the cloud). In one embodiment of the invention, therefore, it is provided that these information sources which have never been present at present are used for the verification of the digital map, and that a plurality of these information sources are used for the verification of the digital map.
In a preferred embodiment, the information provided in respect of the plurality of information sources is filtered and combined by means of a suitable algorithm in order to determine the actual characteristic features.
In addition, the method further includes step S6, in which information about the vehicle position and the difference value is transmitted to the central server.
Advantageously, in the event that the map has been ranked as non-current in step S5, at least one of the following actions is performed:
requesting to update the digital map in the central server;
re-performing steps S3 and S4;
a mapping vehicle, in particular a motor vehicle and/or an aircraft, is requested to map the surroundings of the reference location.
Further, in one embodiment of the invention it is advantageously provided that at least one desired characteristic of the at least one feature is stored in a digital map, wherein preferably a plurality of desired characteristic characteristics of the plurality of features is stored in the digital map, and in step S3 at least one actual characteristic is determined at least partially on the basis of the at least one desired characteristic.
Further, in one embodiment of the invention, it is advantageously provided that the step S3 of providing the desired characteristic feature of the at least one feature comprises selecting the at least one feature from a plurality of possible features, wherein the selection is performed taking into account subsequent steps.
In step S3a, a feature model is created, wherein the feature model describes: the features can be observed with the aid of which information sources are available and under which conditions, in particular at which observation angle and/or at which distance.
In step S3b, a sensor model is created, wherein the sensor model describes: the respectively available information source is currently able to perceive which part of the map at which size, in particular at which resolution and/or at which noise characteristic.
Furthermore, an ambient model is created in step S3c, wherein the ambient model describes: whether a feature can currently be detected or whether the feature is occluded with respect to the information source by a static object or a dynamic object; wherein information about static objects is derived from the digital map and information processed (aufbereitet) by at least one information source is used to determine dynamic objects.
In another embodiment of the present invention, the method comprises the steps of: expected assumptions for the selected features are created from the feature model, the sensor model and the surrounding environment model and verified in step S4.
Advantageously, the features are road markings, guide posts, guardrails, light signalling devices, traffic signs, drivable space, traffic density, 3D world models and/or speed profiles (geschwidtigkeitsprofil).
Advantageously, the desired characteristic feature and the actual characteristic feature are each at least one of the following characteristic features: geographical location, size, color, relative location with respect to the information source.
The device for verifying digital maps of highly automated vehicles (HAF), in particular highly automated vehicles, forms a further subject of the invention. The device comprises at least one information source for detecting actual characteristic features of the features in the surroundings of the reference location, a memory module for storing a digital map (preferably a high-accuracy digital map), wherein the memory module is in particular a central server, and a control device which is provided for exchanging data with the memory module and the at least one information source. According to the invention, the control device is provided to carry out the method according to any one of claims 1 to 11.
Furthermore, a computer program comprising a program code for performing the method according to any one of claims 1 to 11 when the computer program runs on a computer also forms a subject matter of the present invention.
Although the invention is described below primarily in connection with passenger vehicles, the invention is not limited thereto, but can also be used with any type of vehicle, i.e. a load wagon (LKW) and/or a passenger vehicle (PKW).
Further features, application possibilities and advantages of the invention result from the following description of an exemplary embodiment of the invention which is illustrated in the drawings. It should be noted here that the features shown have only a descriptive function and can also be used in combination with the features of the above-described embodiments, and should not be regarded as limiting the invention in any way.
Drawings
The invention will be further elucidated on the basis of a preferred embodiment, wherein the same reference numerals are used for the same features. The figures are schematic and show:
fig. 1 shows a flow chart of a first embodiment of the method according to the invention;
fig. 2 shows an application example of the method according to the invention.
Detailed Description
Fig. 1 shows a flow chart of a first embodiment of the method according to the invention. In this case, in step S1 of fig. 1, a digital map (preferably a high-accuracy digital map) is provided, which can be performed on the device side in a storage module for storing the digital map, in particular a storage module integrated into the HAF or a central server.
Step S2 includes determining a current reference position and locating the reference position in a digital map, as is well known in the art, for example. According to the invention, this is achieved on the device side by means of a location module, wherein the location module is preferably a GPS module (Global Positioning System).
The step labeled S3 in fig. 1 includes determining at least one actual feature characteristic of the feature in the surroundings of the reference location, wherein the determination is performed by means of at least one information source 12, 12 ', 12 ", 12'".
An application example of the method according to the invention is shown in fig. 2, for example, the reference position being the vehicle position of the mapping vehicle 10 shown in fig. 2, wherein the determination of the reference position is carried out by means of at least one information source 12, 12 ', 12 ", 12'".
It is also provided in step S3 that a plurality of information sources 12, 12 ', 12 ", 12'" (fig. 2) are used, wherein the information sources 12, 12 ', 12 ", 12'" comprise at least one or more information sources from the following group of information sources:
a vehicle-to-infrastructure system (C2I) that sends data to or collects data from the vehicle (e.g., over WLAN, LTE);
a vehicle-to-vehicle system (C2C) that transmits data to other vehicles, preferably over a wireless communication network (WLAN) or LTE;
a navigation system that stores road strike, grade, lane of travel (Fahrbahnspur), and infrastructure information in map material;
the system comprises a database in the Internet, wherein road data are preferably stored in the database;
a database in a vehicle's own system in which data can be stored for a long period of time;
a vehicle system (preferably a head unit) that has access to the internet and can retrieve current data from a database.
High-accuracy maps for highly automated or fully automated driving, in which data for positioning tasks (preferably objects with position and size) are stored;
a driver assistance system comprising one or more systems from the following group:
Lane-Keeping-Support (LKS), which seeks a Lane, preferably by means of a camera-based system, and redirects the vehicle back into the Lane without voluntary departure from the Lane;
a traffic sign system that finds a prescribed speed based on a vision system (preferably a video camera);
an object detection system, preferably using a vision sensor (particularly preferably a video camera); a smart phone (in particular a camera of a smart phone).
Preferably, at least one actual characteristic feature is determined based at least in part on the at least one desired characteristic feature.
In principle, the features can be road markings, guide posts, guard rails, light signaling devices, traffic signs, drivable space, traffic density, 3D world models and/or speed profiles. Fig. 2 shows an exemplary lane marking 30. Here, the desired characteristic feature and the actual characteristic feature may each be at least one of the following characteristic features: geographical location, size, color, relative location with respect to the information source.
It is advantageous here if at least one desired characteristic feature of at least one feature is stored in the digital map, wherein preferably a plurality of desired characteristic features of a plurality of features are stored in the digital map.
The method steps of the present invention, labeled step S4, include: the actual characteristic features of the features are compared with the expected characteristic features and at least one difference is evaluated as a result of the comparison.
The digital map may then be validated based at least in part on the difference in step S5, wherein the digital map is ranked as non-current if the difference meets or exceeds a prescribed threshold of deviation and as current if the difference is below the prescribed threshold of deviation.
In terms of multiple information sources, the provided information is filtered and combined by a suitable algorithm to determine the actual feature characteristics.
It is further provided that the method further comprises a step S6, in which information about the vehicle position and the difference is transmitted to a central server.
In case the map has been ranked as non-current in step S5, either an update of the digital map in the central server may be requested, and/or steps S3 and S4 may be re-executed, and/or a mapping vehicle (in particular a motor vehicle and/or an airplane) may be requested to be dispatched to map the surroundings of the reference location.
Furthermore, it is provided that at least one desired characteristic of the at least one feature is stored in the digital map, wherein the plurality of desired characteristic features are in turn stored as a plurality of features in the digital map. Further, at least one actual feature characteristic is determined in step S3 based at least in part on the at least one desired feature characteristic.
In one advantageous embodiment of the invention, the step S3 of providing the desired characteristic feature of the at least one feature comprises a selection of the at least one feature from a plurality of possible features, wherein the selection is carried out taking into account the following steps:
s3a creating a feature model, wherein the feature model describes: the feature can be observed by means of which sensors are available and under which conditions, in particular under which observation angle and/or under which distance;
s3b creates a sensor model, wherein the sensor model describes: the respectively available sensors are currently able to perceive which part of the map at which specification, in particular at which resolution and/or at which noise characteristic;
s3c creating an ambient model, wherein the ambient model describes: whether a feature can currently be detected or is occluded by a static or dynamic object in the surroundings of the HAF, wherein information about the static object is derived from the digital map and the dynamic object is determined from the sensor data processed by the at least one sensor.
Further, in one embodiment of the present invention, it is provided that the method comprises the steps of: desired assumptions for the selected features are created from the feature model, the sensor model and the surrounding environment model and verified in step S4.
As shown in fig. 2, the mapping vehicle 10 is equipped with a location module, preferably a GPS module (global positioning System), and travels on a road section with two lanes 101, 102 to be traveled currently. The reference position is determined by means of at least one information source 12, 12 ', 12 ", 12'". The mapping vehicle 10 may communicate with at least one information source 12, 12 ', 12 ", 12'" on the one hand and other vehicles 20, 22, 24 on the other hand. One advantageous effect of the present invention is: the mapping service may also use data of different information sources, for example. This enables expensive mapping drives to be planned more efficiently by the mapping service.
In this way, it is possible to predict, at a large probability, before starting to detect at least one actual characteristic: whether a probe is expected and which type of probe is involved.
In one embodiment of the invention, it is provided that a plurality of sensors are used to detect at least one characteristic feature. Accordingly, in this embodiment, step S6 includes verifying the digital map while performing fusion on the detection results of the sensors involved in the detection.
The invention is not limited to the embodiments described and shown. Rather, the invention also includes all the modifications that are conventional to a person skilled in the art, which are within the scope of the invention as defined by the claims.
The invention is not limited to the embodiments described and shown. Rather, the invention also includes all modifications conventional to those skilled in the art, which are within the scope of the invention as defined by the claims.
In addition to the embodiments described and illustrated, other embodiments are also conceivable which can comprise other variants and combinations of features.
Claims (13)
1. A method for validating a digital map of a highly automated vehicle (HAF), in particular a highly automated vehicle, comprising the steps of:
s1 providing a digital map, preferably a high accuracy digital map;
s2 determining a current reference position and locating the reference position in the digital map;
s3 determining at least one actual feature characteristic of a feature in the surroundings of the reference location, wherein the determination is performed by means of at least one information source;
s4 compares the actual characteristic feature of the feature with an expected characteristic feature and takes at least one difference as a result of the comparison.
2. Method according to claim 1, characterized in that it comprises the following steps:
s5 validating the digital map based at least in part on the difference, wherein the digital map is ranked as non-current if the difference meets or exceeds a specified threshold of deviation and as current if the difference is below the specified threshold of deviation.
3. The method according to claim 1 or 2, wherein a plurality of information sources are used in said step S3, wherein said information sources comprise at least one or more information sources from the following group of information sources:
a vehicle-to-infrastructure system (C2I) that transmits data to or collects data from vehicles (e.g., over WLAN, LTE);
a vehicle-to-vehicle system (C2C) that transmits data to other vehicles, preferably over a wireless communication network (WLAN) or LTE;
a navigation system that stores road strike, grade, lane and infrastructure information in map material;
the system comprises a database in the Internet, wherein road data are preferably stored in the database in the Internet;
a database in a vehicle-own system in which data can be stored for a long period of time;
vehicle system-preferably an on-board host computer, which has access to the internet and can retrieve current data from a database;
a high-accuracy map for highly automated or fully automated driving, in which data for a positioning task, preferably an object having a position and a size, are stored;
a driver assistance system comprising one or more systems from the following group:
a Lane-Keeping-Support (LKS) system which determines the Lane, preferably by means of a camera-based system, and redirects the vehicle back into the Lane in the event of an involuntary departure from the Lane;
a traffic sign system that finds a prescribed speed based on a vision system, preferably a video camera;
an object detection system, preferably using a vision sensor, particularly preferably a video camera;
a smartphone, in particular a camera for a smartphone.
4. A method according to claim 3, characterized in that the information provided in respect of a plurality of information sources is filtered and combined by means of a suitable algorithm in order to determine the actual characteristic features.
5. The method according to any of the preceding claims, characterized in that the method further comprises the step of:
s6 transmits information about the vehicle position and the difference to a central server.
6. Method according to any of the preceding claims, characterized in that at least one of the following actions is performed in case the map has been ranked as non-current in step S5:
requesting an update of the digital map in a central server;
re-executing the steps S3 and S4;
requesting to dispatch a mapping vehicle, in particular a motor vehicle and/or an airplane, to map the surroundings of the reference location.
7. The method according to any of the preceding claims, wherein at least one desired feature characteristic of the at least one feature is stored in the digital map, wherein a plurality of desired feature characteristics of a preferred plurality of features is stored in the digital map, and wherein in the step S3 the at least one actual feature characteristic is determined based at least in part on the at least one desired feature characteristic.
8. Method according to any of the preceding claims, wherein said step S3 of providing a desired feature characteristic of at least one feature comprises selecting at least one feature from a plurality of possible features, wherein said selecting is performed under consideration of the following steps:
s3a creating a feature model, wherein the feature model describes: the feature can be observed by means of which information sources are available and under which conditions, in particular under which observation angle and/or at which distance;
s3b creating a sensor model, wherein the sensor model describes: which part of the map is currently perceivable by which specification, in particular by which resolution and/or by which noise characteristic, the respectively available information source;
s3c creating an ambient model, wherein the ambient model describes: whether a feature can currently be detected or whether the feature is occluded with respect to an information source by a static object or a dynamic object; wherein information about static objects is derived from the digital map and information processed by the at least one information source is used to derive the dynamic objects.
9. The method according to claim 8, characterized in that it comprises the steps of: a desired hypothesis for the selected feature is created from the feature model, the sensor model and the surrounding environment model, and the desired hypothesis is verified in step S4.
10. Method according to any of the preceding claims, characterized in that the feature is a road marking, a guide post, a guardrail, a light signal device, a traffic sign, a space capable of driving, a traffic density, a 3D world model and/or a speed profile.
11. The method according to any of the preceding claims, characterized in that the desired feature characteristic and the actual feature characteristic are respectively at least one of the following characteristics of the feature: geographical location, size, color, relative location with respect to the information source.
12. An apparatus for validating a digital map of a highly automated vehicle (HAF), in particular a highly automated vehicle, the apparatus comprising:
at least one information source for detecting actual feature characteristics of features in the ambient environment of a reference location;
a storage module for storing a digital map, preferably a high accuracy digital map, wherein the storage module is in particular a central server;
a control device arranged to exchange data with the storage module, the at least one information source,
characterized in that the control device is arranged to perform the method according to any of claims 1 to 11.
13. A computer program comprising program code for performing the method according to any one of claims 1 to 11 when the computer program is run on a computer.
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| DE102017211613.7A DE102017211613A1 (en) | 2017-07-07 | 2017-07-07 | Method for verifying a digital map of a higher automated vehicle (HAF), in particular a highly automated vehicle |
| DE102017211613.7 | 2017-07-07 | ||
| PCT/EP2018/064641 WO2019007605A1 (en) | 2017-07-07 | 2018-06-04 | Method for verifying a digital map in a more highly automated vehicle, corresponding device and computer program |
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| CN110869867A true CN110869867A (en) | 2020-03-06 |
| CN110869867B CN110869867B (en) | 2024-02-20 |
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| CN201880045461.7A Active CN110869867B (en) | 2017-07-07 | 2018-06-04 | Method, apparatus and storage medium for verifying digital map of vehicle |
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| US20220139211A1 (en) * | 2019-01-31 | 2022-05-05 | Pioneer Corporation | Server device, information processing method, information processing program and storage medium |
| DE102019207218A1 (en) * | 2019-05-17 | 2020-11-19 | Robert Bosch Gmbh | Procedure for validating a map currency |
| DE102019209117A1 (en) * | 2019-06-25 | 2020-12-31 | Continental Automotive Gmbh | Method for locating a vehicle |
| CN114556251B (en) * | 2019-11-22 | 2023-11-17 | 华为技术有限公司 | Method and device for determining a passable space for a vehicle |
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Also Published As
| Publication number | Publication date |
|---|---|
| EP3649519A1 (en) | 2020-05-13 |
| US20200182629A1 (en) | 2020-06-11 |
| DE102017211613A1 (en) | 2019-01-10 |
| EP3649519B1 (en) | 2022-05-25 |
| WO2019007605A1 (en) | 2019-01-10 |
| CN110869867B (en) | 2024-02-20 |
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